{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Convolutional Autoencoder\n",
    "\n",
    "Sticking with the MNIST dataset, let's improve our autoencoder's performance using convolutional layers. Again, loading modules and the data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "mnist = input_data.read_data_sets('MNIST_data', validation_size=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f8ea6a82b38>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "img = mnist.train.images[2]\n",
    "plt.imshow(img.reshape((28, 28)), cmap='Greys_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network Architecture\n",
    "\n",
    "The encoder part of the network will be a typical convolutional pyramid. Each convolutional layer will be followed by a max-pooling layer to reduce the dimensions of the layers. The decoder though might be something new to you. The decoder needs to convert from a narrow representation to a wide reconstructed image. For example, the representation could be a 4x4x8 max-pool layer. This is the output of the encoder, but also the input to the decoder. We want to get a 28x28x1 image out from the decoder so we need to work our way back up from the narrow decoder input layer. A schematic of the network is shown below.\n",
    "\n",
    "<img src='images/convolutional_autoencoder.png' width=500px>\n",
    "\n",
    "Here our final encoder layer has size 4x4x8 = 128. The original images have size 28x28 = 784, so the encoded vector is roughly 16% the size of the original image. These are just suggested sizes for each of the layers. Feel free to change the depths and sizes, but remember our goal here is to find a small representation of the input data.\n",
    "### What's going on with the decoder\n",
    "\n",
    "Okay, so the decoder has these \"Upsample\" layers that you might not have seen before. First off, we'll discuss a bit what these layers aren't. Usually, we see transposed convolution layers used to increase the width and height of the layers. They work almost exactly the same as convolutional layers, but in reverse. A stride in the input layer results in a larger stride in the transposed convolution layer. For example, if you have a 3x3 kernel, a 3x3 patch in the input layer will be reduced to one unit in a convolutional layer. Comparatively, one unit in the input layer will be expanded to a 3x3 path in a transposed convolution layer. The TensorFlow API provides us with an easy way to create the layers, tf.nn.conv2d_transpose.\n",
    "\n",
    "However, transposed convolution layers can lead to artifacts in the final images, such as checkerboard patterns. This is due to overlap in the kernels which can be avoided by setting the stride and kernel size equal. In this Distill article from Augustus Odena, et al, the authors show that these checkerboard artifacts can be avoided by resizing the layers using nearest neighbor or bilinear interpolation (upsampling) followed by a convolutional layer. In TensorFlow, this is easily done with tf.image.resize_images, followed by a convolution. Be sure to read the Distill article to get a better understanding of deconvolutional layers and why we're using upsampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='inputs')\n",
    "targets_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='targets')\n",
    "\n",
    "### Encoder\n",
    "conv1 = tf.layers.conv2d(inputs_, 16, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 28x28x16\n",
    "maxpool1 = tf.layers.max_pooling2d(conv1, (2,2), (2,2), padding='same')\n",
    "# Now 14x14x16\n",
    "conv2 = tf.layers.conv2d(maxpool1, 8, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 14x14x8\n",
    "maxpool2 = tf.layers.max_pooling2d(conv2, (2,2), (2,2), padding='same')\n",
    "# Now 7x7x8\n",
    "conv3 = tf.layers.conv2d(maxpool2, 8, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 7x7x8\n",
    "encoded = tf.layers.max_pooling2d(conv3, (2,2), (2,2), padding='same')\n",
    "# Now 4x4x8\n",
    "\n",
    "### Decoder\n",
    "upsample1 = tf.image.resize_nearest_neighbor(encoded, (7,7))\n",
    "# Now 7x7x8\n",
    "conv4 = tf.layers.conv2d(upsample1, 8, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 7x7x8\n",
    "upsample2 = tf.image.resize_nearest_neighbor(conv4, (14,14))\n",
    "# Now 14x14x8\n",
    "conv5 = tf.layers.conv2d(upsample2, 8, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 14x14x8\n",
    "upsample3 = tf.image.resize_nearest_neighbor(conv5, (28,28))\n",
    "# Now 28x28x8\n",
    "conv6 = tf.layers.conv2d(upsample3, 16, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 28x28x16\n",
    "\n",
    "logits = tf.layers.conv2d(conv6, 1, (3,3), padding='same', activation=None)\n",
    "#Now 28x28x1\n",
    "\n",
    "decoded = tf.nn.sigmoid(logits, name='decoded')\n",
    "\n",
    "loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=targets_, logits=logits)\n",
    "cost = tf.reduce_mean(loss)\n",
    "opt = tf.train.AdamOptimizer(0.001).minimize(cost)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Training\n",
    "\n",
    "As before, here we'll train the network. Instead of flattening the images though, we can pass them in as 28x28x1 arrays.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.Session()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/10... Training loss: 0.6958\n",
      "Epoch: 1/10... Training loss: 0.6927\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 10/10... Training loss: 0.1077\n",
      "Epoch: 10/10... Training loss: 0.1106\n",
      "Epoch: 10/10... Training loss: 0.1069\n",
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      "Epoch: 10/10... Training loss: 0.1108\n",
      "Epoch: 10/10... Training loss: 0.1066\n",
      "Epoch: 10/10... Training loss: 0.0998\n",
      "Epoch: 10/10... Training loss: 0.1064\n",
      "Epoch: 10/10... Training loss: 0.1070\n",
      "Epoch: 10/10... Training loss: 0.1066\n",
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      "Epoch: 10/10... Training loss: 0.1065\n",
      "Epoch: 10/10... Training loss: 0.1081\n",
      "Epoch: 10/10... Training loss: 0.1082\n",
      "Epoch: 10/10... Training loss: 0.1037\n"
     ]
    }
   ],
   "source": [
    "epochs = 10\n",
    "batch_size = 200\n",
    "sess.run(tf.global_variables_initializer())\n",
    "for e in range(epochs):\n",
    "    for ii in range(mnist.train.num_examples//batch_size):\n",
    "        batch = mnist.train.next_batch(batch_size)\n",
    "        imgs = batch[0].reshape((-1, 28, 28, 1))\n",
    "        batch_cost, _ = sess.run([cost, opt], feed_dict={inputs_: imgs,\n",
    "                                                         targets_: imgs})\n",
    "\n",
    "        print(\"Epoch: {}/{}...\".format(e+1, epochs),\n",
    "              \"Training loss: {:.4f}\".format(batch_cost))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))\n",
    "in_imgs = mnist.test.images[:10]\n",
    "reconstructed = sess.run(decoded, feed_dict={inputs_: in_imgs.reshape((10, 28, 28, 1))})\n",
    "\n",
    "for images, row in zip([in_imgs, reconstructed], axes):\n",
    "    for img, ax in zip(images, row):\n",
    "        ax.imshow(img.reshape((28, 28)), cmap='Greys_r')\n",
    "        ax.get_xaxis().set_visible(False)\n",
    "        ax.get_yaxis().set_visible(False)\n",
    "\n",
    "\n",
    "fig.tight_layout(pad=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess.close()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Denoising\n",
    "\n",
    "As I've mentioned before, autoencoders like the ones you've built so far aren't too useful in practive. However, they can be used to denoise images quite successfully just by training the network on noisy images. We can create the noisy images ourselves by adding Gaussian noise to the training images, then clipping the values to be between 0 and 1. We'll use noisy images as input and the original, clean images as targets. Here's an example of the noisy images I generated and the denoised images.\n",
    "\n",
    "![title](images/denoising.png)\n",
    "\n",
    "### Denoising autoencoder\n",
    "\n",
    "Since this is a harder problem for the network, we'll want to use deeper convolutional layers here, more feature maps. I suggest something like 32-32-16 for the depths of the convolutional layers in the encoder, and the same depths going backward through the decoder. Otherwise the architecture is the same as before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='inputs')\n",
    "targets_ = tf.placeholder(tf.float32, (None, 28, 28, 1), name='targets')\n",
    "\n",
    "### Encoder\n",
    "conv1 = tf.layers.conv2d(inputs_, 32, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 28x28x32\n",
    "maxpool1 = tf.layers.max_pooling2d(conv1, (2,2), (2,2), padding='same')\n",
    "# Now 14x14x32\n",
    "conv2 = tf.layers.conv2d(maxpool1, 32, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 14x14x32\n",
    "maxpool2 = tf.layers.max_pooling2d(conv2, (2,2), (2,2), padding='same')\n",
    "# Now 7x7x32\n",
    "conv3 = tf.layers.conv2d(maxpool2, 16, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 7x7x16\n",
    "encoded = tf.layers.max_pooling2d(conv3, (2,2), (2,2), padding='same')\n",
    "# Now 4x4x16\n",
    "\n",
    "### Decoder\n",
    "upsample1 = tf.image.resize_nearest_neighbor(encoded, (7,7))\n",
    "# Now 7x7x16\n",
    "conv4 = tf.layers.conv2d(upsample1, 16, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 7x7x16\n",
    "upsample2 = tf.image.resize_nearest_neighbor(conv4, (14,14))\n",
    "# Now 14x14x16\n",
    "conv5 = tf.layers.conv2d(upsample2, 32, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 14x14x32\n",
    "upsample3 = tf.image.resize_nearest_neighbor(conv5, (28,28))\n",
    "# Now 28x28x32\n",
    "conv6 = tf.layers.conv2d(upsample3, 32, (3,3), padding='same', activation=tf.nn.relu)\n",
    "# Now 28x28x32\n",
    "\n",
    "logits = tf.layers.conv2d(conv6, 1, (3,3), padding='same', activation=None)\n",
    "#Now 28x28x1\n",
    "\n",
    "decoded = tf.nn.sigmoid(logits, name='decoded')\n",
    "\n",
    "loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=targets_, logits=logits)\n",
    "cost = tf.reduce_mean(loss)\n",
    "opt = tf.train.AdamOptimizer(0.001).minimize(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.Session()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1/30... Training loss: 0.6962\n",
      "Epoch: 1/30... Training loss: 0.6861\n",
      "Epoch: 1/30... Training loss: 0.6770\n",
      "Epoch: 1/30... Training loss: 0.6639\n",
      "Epoch: 1/30... Training loss: 0.6433\n",
      "Epoch: 1/30... Training loss: 0.6151\n",
      "Epoch: 1/30... Training loss: 0.5791\n",
      "Epoch: 1/30... Training loss: 0.5330\n",
      "Epoch: 1/30... Training loss: 0.4927\n",
      "Epoch: 1/30... Training loss: 0.4781\n",
      "Epoch: 1/30... Training loss: 0.5185\n",
      "Epoch: 1/30... Training loss: 0.5113\n",
      "Epoch: 1/30... Training loss: 0.4830\n",
      "Epoch: 1/30... Training loss: 0.4511\n",
      "Epoch: 1/30... Training loss: 0.4470\n",
      "Epoch: 1/30... Training loss: 0.4327\n",
      "Epoch: 1/30... Training loss: 0.4338\n",
      "Epoch: 1/30... Training loss: 0.4267\n",
      "Epoch: 1/30... Training loss: 0.4097\n",
      "Epoch: 1/30... Training loss: 0.3936\n",
      "Epoch: 1/30... Training loss: 0.3836\n",
      "Epoch: 1/30... Training loss: 0.3734\n",
      "Epoch: 1/30... Training loss: 0.3646\n",
      "Epoch: 1/30... Training loss: 0.3488\n",
      "Epoch: 1/30... Training loss: 0.3342\n",
      "Epoch: 1/30... Training loss: 0.3267\n",
      "Epoch: 1/30... Training loss: 0.3209\n",
      "Epoch: 1/30... Training loss: 0.3075\n",
      "Epoch: 1/30... Training loss: 0.3050\n",
      "Epoch: 1/30... Training loss: 0.3056\n",
      "Epoch: 1/30... Training loss: 0.2879\n",
      "Epoch: 1/30... Training loss: 0.2832\n",
      "Epoch: 1/30... Training loss: 0.2847\n",
      "Epoch: 1/30... Training loss: 0.2790\n",
      "Epoch: 1/30... Training loss: 0.2830\n",
      "Epoch: 1/30... Training loss: 0.2707\n",
      "Epoch: 1/30... Training loss: 0.2719\n",
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      "Epoch: 30/30... Training loss: 0.1047\n",
      "Epoch: 30/30... Training loss: 0.1025\n",
      "Epoch: 30/30... Training loss: 0.1086\n",
      "Epoch: 30/30... Training loss: 0.1013\n",
      "Epoch: 30/30... Training loss: 0.1044\n",
      "Epoch: 30/30... Training loss: 0.0988\n",
      "Epoch: 30/30... Training loss: 0.1049\n",
      "Epoch: 30/30... Training loss: 0.1027\n",
      "Epoch: 30/30... Training loss: 0.1010\n",
      "Epoch: 30/30... Training loss: 0.1005\n",
      "Epoch: 30/30... Training loss: 0.1041\n",
      "Epoch: 30/30... Training loss: 0.1022\n",
      "Epoch: 30/30... Training loss: 0.1037\n",
      "Epoch: 30/30... Training loss: 0.1024\n",
      "Epoch: 30/30... Training loss: 0.1051\n",
      "Epoch: 30/30... Training loss: 0.1038\n",
      "Epoch: 30/30... Training loss: 0.1026\n",
      "Epoch: 30/30... Training loss: 0.1046\n",
      "Epoch: 30/30... Training loss: 0.0962\n",
      "Epoch: 30/30... Training loss: 0.1020\n",
      "Epoch: 30/30... Training loss: 0.1026\n",
      "Epoch: 30/30... Training loss: 0.1036\n",
      "Epoch: 30/30... Training loss: 0.1016\n",
      "Epoch: 30/30... Training loss: 0.1015\n",
      "Epoch: 30/30... Training loss: 0.1031\n",
      "Epoch: 30/30... Training loss: 0.1024\n",
      "Epoch: 30/30... Training loss: 0.1016\n",
      "Epoch: 30/30... Training loss: 0.1068\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 30/30... Training loss: 0.1036\n",
      "Epoch: 30/30... Training loss: 0.1047\n",
      "Epoch: 30/30... Training loss: 0.1067\n",
      "Epoch: 30/30... Training loss: 0.1031\n",
      "Epoch: 30/30... Training loss: 0.1026\n",
      "Epoch: 30/30... Training loss: 0.1041\n",
      "Epoch: 30/30... Training loss: 0.1029\n",
      "Epoch: 30/30... Training loss: 0.1027\n",
      "Epoch: 30/30... Training loss: 0.1011\n",
      "Epoch: 30/30... Training loss: 0.1045\n",
      "Epoch: 30/30... Training loss: 0.1049\n",
      "Epoch: 30/30... Training loss: 0.1019\n",
      "Epoch: 30/30... Training loss: 0.1031\n",
      "Epoch: 30/30... Training loss: 0.0987\n",
      "Epoch: 30/30... Training loss: 0.1002\n",
      "Epoch: 30/30... Training loss: 0.1027\n",
      "Epoch: 30/30... Training loss: 0.1029\n",
      "Epoch: 30/30... Training loss: 0.0986\n",
      "Epoch: 30/30... Training loss: 0.1053\n",
      "Epoch: 30/30... Training loss: 0.1011\n",
      "Epoch: 30/30... Training loss: 0.1023\n",
      "Epoch: 30/30... Training loss: 0.1037\n",
      "Epoch: 30/30... Training loss: 0.1035\n",
      "Epoch: 30/30... Training loss: 0.1025\n",
      "Epoch: 30/30... Training loss: 0.1014\n",
      "Epoch: 30/30... Training loss: 0.1019\n",
      "Epoch: 30/30... Training loss: 0.1014\n",
      "Epoch: 30/30... Training loss: 0.1018\n",
      "Epoch: 30/30... Training loss: 0.1040\n",
      "Epoch: 30/30... Training loss: 0.1037\n",
      "Epoch: 30/30... Training loss: 0.1047\n",
      "Epoch: 30/30... Training loss: 0.1028\n",
      "Epoch: 30/30... Training loss: 0.1052\n",
      "Epoch: 30/30... Training loss: 0.1064\n",
      "Epoch: 30/30... Training loss: 0.1045\n",
      "Epoch: 30/30... Training loss: 0.1066\n",
      "Epoch: 30/30... Training loss: 0.1043\n",
      "Epoch: 30/30... Training loss: 0.1031\n",
      "Epoch: 30/30... Training loss: 0.1023\n",
      "Epoch: 30/30... Training loss: 0.0974\n",
      "Epoch: 30/30... Training loss: 0.1029\n",
      "Epoch: 30/30... Training loss: 0.1013\n",
      "Epoch: 30/30... Training loss: 0.1019\n",
      "Epoch: 30/30... Training loss: 0.1073\n",
      "Epoch: 30/30... Training loss: 0.1033\n",
      "Epoch: 30/30... Training loss: 0.1028\n",
      "Epoch: 30/30... Training loss: 0.1031\n",
      "Epoch: 30/30... Training loss: 0.1047\n",
      "Epoch: 30/30... Training loss: 0.1029\n",
      "Epoch: 30/30... Training loss: 0.1018\n",
      "Epoch: 30/30... Training loss: 0.1015\n",
      "Epoch: 30/30... Training loss: 0.0975\n",
      "Epoch: 30/30... Training loss: 0.1003\n",
      "Epoch: 30/30... Training loss: 0.1028\n",
      "Epoch: 30/30... Training loss: 0.1023\n",
      "Epoch: 30/30... Training loss: 0.1040\n",
      "Epoch: 30/30... Training loss: 0.1047\n",
      "Epoch: 30/30... Training loss: 0.1015\n",
      "Epoch: 30/30... Training loss: 0.1044\n",
      "Epoch: 30/30... Training loss: 0.1012\n",
      "Epoch: 30/30... Training loss: 0.1007\n",
      "Epoch: 30/30... Training loss: 0.1010\n",
      "Epoch: 30/30... Training loss: 0.1051\n",
      "Epoch: 30/30... Training loss: 0.1050\n",
      "Epoch: 30/30... Training loss: 0.1027\n"
     ]
    }
   ],
   "source": [
    "epochs = 30\n",
    "batch_size = 200\n",
    "# Set's how much noise we're adding to the MNIST images\n",
    "noise_factor = 0.5\n",
    "sess.run(tf.global_variables_initializer())\n",
    "for e in range(epochs):\n",
    "    for ii in range(mnist.train.num_examples//batch_size):\n",
    "        batch = mnist.train.next_batch(batch_size)\n",
    "        # Get images from the batch\n",
    "        imgs = batch[0].reshape((-1, 28, 28, 1))\n",
    "        \n",
    "        # Add random noise to the input images\n",
    "        noisy_imgs = imgs + noise_factor * np.random.randn(*imgs.shape)\n",
    "        # Clip the images to be between 0 and 1\n",
    "        noisy_imgs = np.clip(noisy_imgs, 0., 1.)\n",
    "        \n",
    "        # Noisy images as inputs, original images as targets\n",
    "        batch_cost, _ = sess.run([cost, opt], feed_dict={inputs_: noisy_imgs,\n",
    "                                                         targets_: imgs})\n",
    "\n",
    "        print(\"Epoch: {}/{}...\".format(e+1, epochs),\n",
    "              \"Training loss: {:.4f}\".format(batch_cost))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Checking out the performance\n",
    "\n",
    "Here I'm adding noise to the test images and passing them through the autoencoder. It does a suprisingly great job of removing the noise, even though it's sometimes difficult to tell what the original number is.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x288 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))\n",
    "in_imgs = mnist.test.images[:10]\n",
    "noisy_imgs = in_imgs + noise_factor * np.random.randn(*in_imgs.shape)\n",
    "noisy_imgs = np.clip(noisy_imgs, 0., 1.)\n",
    "\n",
    "reconstructed = sess.run(decoded, feed_dict={inputs_: noisy_imgs.reshape((10, 28, 28, 1))})\n",
    "\n",
    "for images, row in zip([noisy_imgs, reconstructed], axes):\n",
    "    for img, ax in zip(images, row):\n",
    "        ax.imshow(img.reshape((28, 28)), cmap='Greys_r')\n",
    "        ax.get_xaxis().set_visible(False)\n",
    "        ax.get_yaxis().set_visible(False)\n",
    "\n",
    "fig.tight_layout(pad=0.1)"
   ]
  }
 ],
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